title: Using computational patients to evaluate illness mechanisms in schizophrenia.
abstract: BACKGROUND
  Various malfunctions involving working memory, semantics, prediction error, and dopamine neuromodulation have been hypothesized to cause disorganized speech and delusions in schizophrenia. Computational models may provide insights into why some mechanisms are unlikely, suggest alternative mechanisms, and tie together explanations of seemingly disparate symptoms and experimental findings.
  
  
  METHODS
  Eight corresponding illness mechanisms were simulated in DISCERN, an artificial neural network model of narrative understanding and recall. For this study, DISCERN learned sets of autobiographical and impersonal crime stories with associated emotion coding. In addition, 20 healthy control subjects and 37 patients with schizophrenia or schizoaffective disorder matched for age, gender, and parental education were studied using a delayed story recall task. A goodness-of-fit analysis was performed to determine the mechanism best reproducing narrative breakdown profiles generated by healthy control subjects and patients with schizophrenia. Evidence of delusion-like narratives was sought in simulations best matching the narrative breakdown profile of patients.
  
  
  RESULTS
  All mechanisms were equivalent in matching the narrative breakdown profile of healthy control subjects. However, exaggerated prediction-error signaling during consolidation of episodic memories, termed hyperlearning, was statistically superior to other mechanisms in matching the narrative breakdown profile of patients. These simulations also systematically confused autobiographical agents with impersonal crime story agents to model fixed, self-referential delusions.
  
  
  CONCLUSIONS
  Findings suggest that exaggerated prediction-error signaling in schizophrenia intermingles and corrupts narrative memories when incorporated into long-term storage, thereby disrupting narrative language and producing fixed delusional narratives. If further validated by clinical studies, these computational patients could provide a platform for developing and testing novel treatments.
authors.affiliation: Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 06519, USA. ralph.hoffman@yale.edu
authors.forename: Ralph E
authors.initials: RE
authors.lastname: Hoffman
authors.forename: Uli
authors.initials: U
authors.lastname: Grasemann
authors.forename: Ralitza
authors.initials: R
authors.lastname: Gueorguieva
authors.forename: Donald
authors.initials: D
authors.lastname: Quinlan
authors.forename: Douglas
authors.initials: D
authors.lastname: Lane
authors.forename: Risto
authors.initials: R
authors.lastname: Miikkulainen
mesh_terms: D000328:Adult; D003072:Cognition Disorders / Q000209:etiology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D011939:Mental Recall / Q000502:physiology; D008875:Middle Aged; D008954:Models, Biological; D012559:Schizophrenia / Q000150:complications* / Q000175:diagnosis*